Cyber Center PublicationsCopyright (c) 2015 Purdue University All rights reserved.http://docs.lib.purdue.edu/ccpubs
Recent documents in Cyber Center Publicationsen-usThu, 12 Feb 2015 01:35:29 PST3600Effective Key Management in Dynamic Wireless Sensor Networkshttp://docs.lib.purdue.edu/ccpubs/641
http://docs.lib.purdue.edu/ccpubs/641Tue, 10 Feb 2015 12:55:40 PST
Recently, wireless sensor networks (WSNs) have been deployed for a wide variety of applications, including military sensing and tracking, patient status monitoring, traffic flow monitoring, where sensory devices often move between different locations. Securing data and communications requires suitable encryption key protocols. In this paper, we propose a certificateless-effective key management (CL-EKM) protocol for secure communication in dynamic WSNs characterized by node mobility. The CL-EKM supports efficient key updates when a node leaves or joins a cluster and ensures forward and backward key secrecy. The protocol also supports efficient key revocation for compromised nodes and minimizes the impact of a node compromise on the security of other communication links. A security analysis of our scheme shows that our protocol is effective in defending against various attacks. We implement CL-EKM in Contiki OS and simulate it using Cooja simulator to assess its time, energy, communication, and memory performance.
]]>
Seung-Hyun Seo et al.Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attackshttp://docs.lib.purdue.edu/ccpubs/640
http://docs.lib.purdue.edu/ccpubs/640Tue, 10 Feb 2015 12:55:39 PST
Due to limited computational power and energy resources, aggregation of data from multiple sensor nodes done at the aggregating node is usually accomplished by simple methods such as averaging. However such aggregation is known to be highly vulnerable to node compromising attacks. Since WSN are usually unattended and without tamper resistant hardware, they are highly susceptible to such attacks. Thus, ascertaining trustworthiness of data and reputation of sensor nodes is crucial for WSN. As the performance of very low power processors dramatically improves, future aggregator nodes will be capable of performing more sophisticated data aggregation algorithms, thus making WSN less vulnerable. Iterative filtering algorithms hold great promise for such a purpose. Such algorithms simultaneously aggregate data from multiple sources and provide trust assessment of these sources, usually in a form of corresponding weight factors assigned to data provided by each source. In this paper we demonstrate that several existing iterative filtering algorithms, while significantly more robust against collusion attacks than the simple averaging methods, are nevertheless susceptive to a novel sophisticated collusion attack we introduce. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging.
]]>
Mohsen Rezvani et al.Genetic Determinants for Enzymatic Digestion of Lignocellulosic Biomass Are Independent of Those for Lignin Abundance in a Maize Recombinant Inbred Populationhttp://docs.lib.purdue.edu/ccpubs/639
http://docs.lib.purdue.edu/ccpubs/639Thu, 08 Jan 2015 08:06:07 PST
Biotechnological approaches to reduce or modify lignin in biomass crops are predicated on the assumption that it is the principal determinant of the recalcitrance of biomass to enzymatic digestion for biofuels production. We defined quantitative trait loci (QTL) in the Intermated B73 × Mo17 recombinant inbred maize (Zea mays) population using pyrolysis molecular-beam mass spectrometry to establish stem lignin content and an enzymatic hydrolysis assay to measure glucose and xylose yield. Among five multiyear QTL for lignin abundance, two for 4-vinylphenol abundance, and four for glucose and/or xylose yield, not a single QTL for aromatic abundance and sugar yield was shared. A genome-wide association study for lignin abundance and sugar yield of the 282-member maize association panel provided candidate genes in the 11 QTL of the B73 and Mo17 parents but showed that many other alleles impacting these traits exist among this broader pool of maize genetic diversity. B73 and Mo17 genotypes exhibited large differences in gene expression in developing stem tissues independent of allelic variation. Combining these complementary genetic approaches provides a narrowed list of candidate genes. A cluster of SCARECROW-LIKE9 and SCARECROW-LIKE14 transcription factor genes provides exceptionally strong candidate genes emerging from the genome-wide association study. In addition to these and genes associated with cell wall metabolism, candidates include several other transcription factors associated with vascularization and fiber formation and components of cellular signaling pathways. These results provide new insights and strategies beyond the modification of lignin to enhance yields of biofuels from genetically modified biomass.
]]>
Bryan W. Penning et al.ERUPT: Energy-efficient tRUstworthy Provenance Trees for Wireless Sensor Networkshttp://docs.lib.purdue.edu/ccpubs/638
http://docs.lib.purdue.edu/ccpubs/638Tue, 16 Dec 2014 08:30:07 PST
Sensor nodes are inherently unreliable and prone to

hardware or software faults. Thus, they may report untrustwor-

thy or inconsistent data. Assessing the trustworthiness of sensor data items can allow reliable sensing or monitoring of physical phenomena. A provenance-based trust framework can evaluate the trustworthiness of data items and sensor nodes based on the intuition that two data items with similar data values but with different provenance (i.e., forwarding path) can be considered more trustworthy. Forwarding paths of data items generated from redundantly deployed sensors should consist of trustworthy nodes and remain dissimilar. Unfortunately, operating many sensors with dissimilar paths consumes significant energy. In this paper, we formulate an optimization problem to identify a set of sensor nodes and their corresponding paths toward the base station that achieve a certain trustworthiness threshold, while keeping the energy consumption of the network minimal. We prove the NP-hardness of this problem and propose ERUPT, a simulated annealing solution. Testbed and simulation results show that ERUPT achieves high trustworthiness, while reducing total energy consumption by 32-50% with respect to current approaches.

]]>
S.M. Iftekharul Alam et al.Theory-Inspired Optimizations for Privacy Preserving Distributed OLAP Algorithmshttp://docs.lib.purdue.edu/ccpubs/637
http://docs.lib.purdue.edu/ccpubs/637Mon, 15 Dec 2014 11:38:39 PST
Actually, a lot of attention focusing on the problem of computing privacy-preserving OLAP cubes effectively and efficiently arises. State-of-theart proposals rather focus on an algorithmic vision of the problem, and neglect relevant theoretical aspects the investigated problem introduces naturally. In order to fulfill this gap, in this paper we provide algorithms for supporting privacy- preserving OLAP in distributed environments, based on the well-known CUR matrix decomposition method, enriched by some relevant theory-inspired optimizations that look at the intrinsic nature of the investigated problem in order to gain significant benefits, at both the (privacy-preserving) cube computation level and the (privacy-preserving) cube delivery level.
]]>
Alfredo Cuzzocrea et al.Provenance-aware security risk analysis for hosts and network flowshttp://docs.lib.purdue.edu/ccpubs/636
http://docs.lib.purdue.edu/ccpubs/636Mon, 15 Dec 2014 11:38:35 PST
Detection of high risk network flows and high risk hosts is becoming ever more important and more challenging. In order to selectively apply deep packet inspection (DPI) one has to isolate in real time high risk network activities within a huge number of monitored network flows. To help address this problem, we propose an iterative methodology for a simultaneous assessment of risk scores for both hosts and network flows. The proposed approach measures the risk scores of hosts and flows in an interdependent manner; thus, the risk score of a flow influences the risk score of its source and destination hosts, and also the risk score of a host is evaluated by taking into account the risk scores of flows initiated by or terminated at the host. Our experimental results show that such an approach not only effective in detecting high risk hosts and flows but, when deployed in high throughput networks, is also more efficient than PageRank based algorithms.
]]>
Mohsen Rezvani et al.Privacy Preserving Biometrics-Based and User Centric Authentication Protocolhttp://docs.lib.purdue.edu/ccpubs/635
http://docs.lib.purdue.edu/ccpubs/635Mon, 15 Dec 2014 11:38:31 PST
We propose a privacy preserving biometrics-based authentication protocol by which users can authenticate to different service providers from their own devices without involving identity providers in the transactions. Authentication is performed through a zero-knowledge proof of knowledge protocol which is based on a cryptographic identity token created using the unique, repeatable and revocable biometric identifier of the user and a secret provided by the user which enables two-factor authentication as well. Our approach for generating biometric identifiers from the user’s biometric image is based on the support vector machine classification technique in conjunction with a mechanism for feature extraction from the biometric image. The paper includes experimental results on a dataset of iris images and a security and privacy analysis of the protocol.
]]>
Hasini Gunsinghe et al.A Comprehensive Theoretical Framework for Privacy Preserving Distributed OLAPhttp://docs.lib.purdue.edu/ccpubs/634
http://docs.lib.purdue.edu/ccpubs/634Mon, 15 Dec 2014 11:38:27 PST
This paper complements the privacy preserving distributed OLAP framework proposed by us in a previous work by introducing four major theoretical properties that extend models and algorithms presented in the previous work, where the experimental validation of the framework has also been reported. Particularly, our framework makes use of the CUR matrix decomposition technique as the elementary component for computing privacy preserving two-dimensional OLAP views effectively and efficiently. Here, we investigate theoretical properties of the CUR decomposition method, and identify four theoretical extensions of this method, which, according to our vision, may result in benefits for a wide spectrum of aspects in the context of privacy preserving distributed OLAP, such as privacy preserving knowledge fruition schemes and query optimization. In addition to this, we also provide a widespread experimental analysis of the framework, which fully confirms to us the major practical achievements, in terms of both efficacy and efficiency, due to our framework.
]]>
Alfredo Cuzzocrea et al.Randomized and Efficient Authentication in Mobile Environmentshttp://docs.lib.purdue.edu/ccpubs/633
http://docs.lib.purdue.edu/ccpubs/633Mon, 15 Dec 2014 11:38:22 PST
In a mobile environment, a number of users act as a network nodes and communicate with one another to acquire location based information and services. This emerging paradigm has opened up new business opportunities and enables numerous applications such as road safety enhancement, service recommendations and mobile entertainment. A fundamental issue that impacts the success of these applications is the security and privacy concerns raised regarding the mobile users. In that, a malicious user or service provider can track the locations of a user traveled so that other malicious act can be carried out more effectively against the user. Therefore, the challenge becomes how to authenticate mobile users while preserving their actual identity and location privacy. In this work, we propose a novel randomized or privacy-preserving authentication protocol based on homomorphic encryption. The protocol allows individual users to self generate any number of authenticated identities to achieve full anonymity in mobile environment. The proposed protocol prevents users being tracked by any single party including peer users, service providers, authentication servers, and other infrastructure. Meanwhile, our protocol also provides traceability in case of any dispute. We have conducted experimental study which demonstrates the efficiency of our protocol. Another advantage of the proposed protocol is lightweight computation and storage requirement, particularly suitable for any mobile devices with limited computation power and storage space.
]]>
Wei Jiang et al.Privacy-Preserving Complex Query Evaluation over Semantically Secure Encrypted Datahttp://docs.lib.purdue.edu/ccpubs/632
http://docs.lib.purdue.edu/ccpubs/632Mon, 15 Dec 2014 10:15:32 PST
In the last decade, several techniques have been proposed to evaluate different types of queries (e.g., range and aggregate queries) over encrypted data in a privacy-preserving manner. However, solutions supporting the privacy-preserving evaluation of complex queries over encrypted data have been developed only recently. Such recent techniques, however, are either insecure or not feasible for practical applications. In this paper, we propose a novel privacy-preserving query processing framework that supports complex queries over encrypted data in the cloud computing environment and addresses the shortcomings of previous approaches. At a high level, our framework utilizes both homomorphic encryption and garbled circuit techniques at different stages in query processing to achieve the best performance, while at the same time protecting the confidentiality of data, privacy of the user’s input query and hiding data access patterns. Also, as a part of query processing, we provide an efficient approach to systematically combine the predicate results (in encrypted form) of a query to derive the corresponding query evaluation result in a privacy-preserving manner. We theoretically and empirically analyze the performance of this approach and demonstrate its practical value over the current state-of-the-art techniques. Our proposed framework is very efficient from the user’s perspective, thus allowing a user to issue queries even using a resource constrained device (e.g., PDAs and cell phones)
]]>
Bharath Samanthula et al.Dynamic Privacy Policy Management in Services-Based Interactionshttp://docs.lib.purdue.edu/ccpubs/631
http://docs.lib.purdue.edu/ccpubs/631Fri, 12 Dec 2014 13:06:52 PST
Technology advancements have enabled the distribution and sharing of patient personal health data over several data sources. Each data source is potentially managed by a different organization, which expose its data as aWeb service. Using suchWeb services, dynamic composition of atomic data type properties coupled with the context in which the data is accessed may breach sensitive data that may not comply with the users preference at the time of data collection. Thus, providing uniform access policies to such data can lead to privacy problems. Some fairly recent research has focused on providing solutions for dynamic privacy policy management. This paper advances these techniques, and fills some gaps in the existing works. In particular, dynamically incorporating user access context into the privacy policy decision, and its enforcement. We provide a formal model definition of the proposed approach and a preliminary evaluation of the model.
]]>
Nariman Ammar et al.ID-Based Group Password-Authenticated Key Exchangehttp://docs.lib.purdue.edu/ccpubs/630
http://docs.lib.purdue.edu/ccpubs/630Fri, 12 Dec 2014 13:06:49 PST
In two-server password-authenticated key exchange (PAKE) protocol, a client splits its password and stores two shares of its password in the two servers, respectively, and the two servers then cooperate to authenticate the client without knowing the password of the client. In case one server is compromised by an adversary, the password of the client is required to remain secure. In this paper, we present a compiler that transforms any two-party PAKE protocol to a two-server PAKE protocol. This compiler is mainly built on two-party PAKE and identity-based encryption (IBE), where the identities of the two servers are used as their public keys. By our compiler, we can construct a two-server PAKE protocol which achieves implicit authentication with only two communications between the client and the servers. As long as the underlying two-party PAKE protocol and IBE scheme have provable security without random oracles, the two-server PAKE protocol constructed by our compiler can be proven to be secure without random oracles.
]]>
Xun Yi et al.Elite Size and Resilience Impact on Global System Structuration in Social Mediahttp://docs.lib.purdue.edu/ccpubs/629
http://docs.lib.purdue.edu/ccpubs/629Fri, 12 Dec 2014 12:19:10 PST
The paper examines the role played by the most productive members of social media systems on leading the project and influencing the degree of project structuration. The paper focuses on findings of a large computational social science project that examines Wikipedia.1
]]>
Sorin Matei et al.POSTER: A Pairing-free Certificateless Hybrid Sign- Cryption Scheme for Advanced Metering Infrastructureshttp://docs.lib.purdue.edu/ccpubs/628
http://docs.lib.purdue.edu/ccpubs/628Fri, 12 Dec 2014 11:36:31 PST
CertificateLess Hybrid SignCryption (CL-HSC) scheme is useful for efficiently encapsulating symmetric keys for secure communications. It solves the key escrow problem and the certificate management problem. However, the existing scheme is not suitable for Advanced Metering Infrastructure (AMI) networks because of the utilization of expensive pairing operations. As smart meter devices have limited computing power, we need efficient algorithms for AMI net- works. In this poster, we propose a novel CL-HSC scheme without pairing operations. In order to evaluate its performance, we implemented our CL-HSC scheme and conventional hybrid encryption approaches. The experimental results show that our CL-HSC scheme is efficient and suitable for secure communications in AMI networks.
]]>
Seung-Hyun Seo et al.Security of Graph Data: Hashing Schemes and Definitionshttp://docs.lib.purdue.edu/ccpubs/627
http://docs.lib.purdue.edu/ccpubs/627Fri, 12 Dec 2014 11:36:26 PST
Use of graph-structured data models is on the rise - in graph databases, in representing biological and healthcare data as well as geographical data. In order to secure graph-structured data, and develop cryptographically secure schemes for graph databases, it is essential to formally define and develop suitable collision resistant one-way hashing schemes and show them they are efficient. The widely used Merkle hash technique is not suitable as it is, because graphs may be directed acyclic ones or cyclic ones. In this paper, we are addressing this problem. Our contributions are: (1) define the practical and formal security model of hashing schemes for graphs, (2) define the formal security model of perfectly secure hashing schemes, (3) describe constructions of hashing and perfectly secure hashing of graphs, and (4) performance results for the constructions. Our constructions use graph traversal techniques, and are highly efficient for hashing, redaction, and verification of hashes graphs. We have implemented the proposed schemes, and our performance analysis on both real and synthetic graph data sets support our claims.
]]>
Muhammad U. Arshad et al.Security with Privacy - Opportunities and Challengeshttp://docs.lib.purdue.edu/ccpubs/626
http://docs.lib.purdue.edu/ccpubs/626Fri, 12 Dec 2014 11:36:22 PST
This paper summarizes opportunities and challenges concerning how we can achieve security while still ensuring privacy. It identifies research directions and includes a number of questions that have been debated by the panel.
]]>
Elisa BertinoSecure kNN Query Processing in Untrusted Cloud Environmentshttp://docs.lib.purdue.edu/ccpubs/625
http://docs.lib.purdue.edu/ccpubs/625Fri, 12 Dec 2014 11:03:23 PST
Mobile devices with geo-positioning capabilities (e.g., GPS) enable users to access information that is relevant to their present location. Users are interested in querying about points of interest (POI) in their physical proximity, such as restaurants, cafes, ongoing events, etc. Entities specialized in various areas of interest (e.g., certain niche directions in arts, entertainment, travel) gather large amounts of geo-tagged data that appeal to subscribed users. Such data may be sensitive due to their contents. Furthermore, keeping such information up-to-date and relevant to the users is not an easy task, so the owners of such datasets will make the data accessible only to paying customers. Users send their current location as the query parameter, and wish to receive as result the nearest POIs, i.e., nearest-neighbors (NNs). But typical data owners do not have the technical means to support processing queries on a large scale, so they outsource data storage and querying to a cloud service provider. Many such cloud providers exist who offer powerful storage and computational infrastructures at low cost. However, cloud providers are not fully trusted, and typically behave in an honest-but-curious fashion. Specifically, they follow the protocol to answer queries correctly, but they also collect the locations of the POIs and the subscribers for other purposes. Leakage of POI locations can lead to privacy breaches as well as financial losses to the data owners, for whom the POI dataset is an important source of revenue. Disclosure of user locations leads to privacy violations and may deter subscribers from using the service altogether. In this paper, we propose a family of techniques that allow processing of NN queries in an untrusted outsourced environment, while at the same time protecting both the POI and querying users’ positions. Our techniques rely on mutable order preserving encoding (mOPE), the only secure order-preserving encryption method known to-date. We also provide performance optimizations to decrease the computational cost inherent to processing on encrypted data, and we consider the case of incrementally updating datasets. We present an extensive performance evaluation of our techniques to illustrate their viability in practice.
]]>
Sunoh ChoiDemo Overview: Privacy-Enhancing Features of IdentiDroidhttp://docs.lib.purdue.edu/ccpubs/624
http://docs.lib.purdue.edu/ccpubs/624Fri, 12 Dec 2014 11:03:19 PST
As privacy today is a major concern for mobile systems, network anonymizers are widely available on smartphones systems, such as Android. However, in many cases applications are still able to identify the user and the device by means different from the IP address. In this demo we show two solutions that address this problem by providing application-level anonymity. The first solution shadows sensitive data that can reveal the user identity. The second solution dynamically revokes Android application permissions associated with sensitive information at run-time. In addition, both solutions offer protection from applications that identify their users through traces left in the application's data storage or by exchanging identifying data messages. We developed IdentiDroid, a customized Android operating system, to deploy these solutions, and built IdentiDroid Profile Manager, a profile-based configuration tool for setting different configurations for each installed Android application.
]]>
Daniele Midi et al.POSTER: Protecting Against Data Exfiltration Insider Attacks Through Application Programshttp://docs.lib.purdue.edu/ccpubs/623
http://docs.lib.purdue.edu/ccpubs/623Fri, 12 Dec 2014 11:03:15 PST
In this paper, we describe a system that distinguishes be- tween legitimate and malicious database transactions per- formed by application programs. Our system is particularly useful for protecting against code-modification attacks performed by insiders who have access to and can change the programs' source code to make them execute different queries than those they are expected to execute. Our system works with any type of DBMS and requires minimum modification to application programs.
]]>
Asmaa Mohamed Sallama et al.A Supermodularity-Based Differential Privacy Preserving Algorithm for Data Anonymizationhttp://docs.lib.purdue.edu/ccpubs/622
http://docs.lib.purdue.edu/ccpubs/622Fri, 12 Dec 2014 08:25:47 PST
Maximizing data usage and minimizing privacy risk are two conflicting goals. Organizations always apply a set of transformations on their data before releasing it. While determining the best set of transformations has been the focus of extensive work in the database community, most of this work suffered from one or both of the following major problems: scalability and privacy guarantee. Differential Privacy provides a theoretical formulation for privacy that ensures that the system essentially behaves the same way regardless of whether any individual is included in the database. In this paper, we address both scalability and privacy risk of data anonymization. We propose a scalable algorithm that meets differential privacy when applying a specific random sampling. The contribution of the paper is two-fold: 1) we propose a personalized anonymization technique based on an aggregate formulation and prove that it can be implemented in polynomial time; and 2) we show that combining the proposed aggregate formulation with specific sampling gives an anonymization algorithm that satisfies differential privacy. Our results rely heavily on exploring the supermodularity properties of the risk function, which allow us to employ techniques from convex optimization. Through experimental studies we compare our proposed algorithm with other anonymization schemes in terms of both time and privacy risk.
]]>
Mohamed R Fouad et al.